Overview

Dataset statistics

Number of variables21
Number of observations41188
Missing cells0
Missing cells (%)0.0%
Duplicate rows12
Duplicate rows (%)< 0.1%
Total size in memory6.6 MiB
Average record size in memory168.0 B

Variable types

Numeric10
Categorical10
Boolean1

Alerts

Dataset has 12 (< 0.1%) duplicate rowsDuplicates
cons.conf.idx is highly overall correlated with monthHigh correlation
cons.price.idx is highly overall correlated with contact and 2 other fieldsHigh correlation
contact is highly overall correlated with cons.price.idx and 1 other fieldsHigh correlation
emp.var.rate is highly overall correlated with cons.price.idx and 3 other fieldsHigh correlation
euribor3m is highly overall correlated with emp.var.rate and 2 other fieldsHigh correlation
housing is highly overall correlated with loanHigh correlation
loan is highly overall correlated with housingHigh correlation
month is highly overall correlated with cons.conf.idx and 4 other fieldsHigh correlation
nr.employed is highly overall correlated with emp.var.rate and 1 other fieldsHigh correlation
pdays is highly overall correlated with poutcome and 1 other fieldsHigh correlation
poutcome is highly overall correlated with pdaysHigh correlation
previous is highly overall correlated with pdaysHigh correlation
default is highly imbalanced (53.3%)Imbalance
loan is highly imbalanced (51.3%)Imbalance
poutcome is highly imbalanced (56.8%)Imbalance
previous has 35563 (86.3%) zerosZeros

Reproduction

Analysis started2024-08-21 06:25:09.668048
Analysis finished2024-08-21 06:25:44.552676
Duration34.88 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

age
Real number (ℝ)

Distinct78
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.02406
Minimum17
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.9 KiB
2024-08-21T13:25:44.984521image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile26
Q132
median38
Q347
95-th percentile58
Maximum98
Range81
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.42125
Coefficient of variation (CV)0.26037463
Kurtosis0.79131153
Mean40.02406
Median Absolute Deviation (MAD)7
Skewness0.78469682
Sum1648511
Variance108.60245
MonotonicityNot monotonic
2024-08-21T13:25:45.253802image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 1947
 
4.7%
32 1846
 
4.5%
33 1833
 
4.5%
36 1780
 
4.3%
35 1759
 
4.3%
34 1745
 
4.2%
30 1714
 
4.2%
37 1475
 
3.6%
29 1453
 
3.5%
39 1432
 
3.5%
Other values (68) 24204
58.8%
ValueCountFrequency (%)
17 5
 
< 0.1%
18 28
 
0.1%
19 42
 
0.1%
20 65
 
0.2%
21 102
 
0.2%
22 137
 
0.3%
23 226
 
0.5%
24 463
1.1%
25 598
1.5%
26 698
1.7%
ValueCountFrequency (%)
98 2
 
< 0.1%
95 1
 
< 0.1%
94 1
 
< 0.1%
92 4
 
< 0.1%
91 2
 
< 0.1%
89 2
 
< 0.1%
88 22
0.1%
87 1
 
< 0.1%
86 8
 
< 0.1%
85 15
< 0.1%

job
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
admin.
10422 
blue-collar
9254 
technician
6743 
services
3969 
management
2924 
Other values (7)
7876 

Length

Max length13
Median length12
Mean length8.9552297
Min length6

Characters and Unicode

Total characters368848
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhousemaid
2nd rowservices
3rd rowservices
4th rowadmin.
5th rowservices

Common Values

ValueCountFrequency (%)
admin. 10422
25.3%
blue-collar 9254
22.5%
technician 6743
16.4%
services 3969
 
9.6%
management 2924
 
7.1%
retired 1720
 
4.2%
entrepreneur 1456
 
3.5%
self-employed 1421
 
3.5%
housemaid 1060
 
2.6%
unemployed 1014
 
2.5%
Other values (2) 1205
 
2.9%

Length

2024-08-21T13:25:45.511468image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
admin 10422
25.3%
blue-collar 9254
22.5%
technician 6743
16.4%
services 3969
 
9.6%
management 2924
 
7.1%
retired 1720
 
4.2%
entrepreneur 1456
 
3.5%
self-employed 1421
 
3.5%
housemaid 1060
 
2.6%
unemployed 1014
 
2.5%
Other values (2) 1205
 
2.9%

Most occurring characters

ValueCountFrequency (%)
e 47273
12.8%
n 35547
 
9.6%
a 33327
 
9.0%
l 31618
 
8.6%
i 30657
 
8.3%
c 26709
 
7.2%
r 21031
 
5.7%
m 19765
 
5.4%
d 16512
 
4.5%
t 14593
 
4.0%
Other values (14) 91816
24.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 368848
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 47273
12.8%
n 35547
 
9.6%
a 33327
 
9.0%
l 31618
 
8.6%
i 30657
 
8.3%
c 26709
 
7.2%
r 21031
 
5.7%
m 19765
 
5.4%
d 16512
 
4.5%
t 14593
 
4.0%
Other values (14) 91816
24.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 368848
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 47273
12.8%
n 35547
 
9.6%
a 33327
 
9.0%
l 31618
 
8.6%
i 30657
 
8.3%
c 26709
 
7.2%
r 21031
 
5.7%
m 19765
 
5.4%
d 16512
 
4.5%
t 14593
 
4.0%
Other values (14) 91816
24.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 368848
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 47273
12.8%
n 35547
 
9.6%
a 33327
 
9.0%
l 31618
 
8.6%
i 30657
 
8.3%
c 26709
 
7.2%
r 21031
 
5.7%
m 19765
 
5.4%
d 16512
 
4.5%
t 14593
 
4.0%
Other values (14) 91816
24.9%

marital
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
married
24928 
single
11568 
divorced
4612 
unknown
 
80

Length

Max length8
Median length7
Mean length6.8311159
Min length6

Characters and Unicode

Total characters281360
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmarried
2nd rowmarried
3rd rowmarried
4th rowmarried
5th rowmarried

Common Values

ValueCountFrequency (%)
married 24928
60.5%
single 11568
28.1%
divorced 4612
 
11.2%
unknown 80
 
0.2%

Length

2024-08-21T13:25:45.759781image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-21T13:25:45.985179image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
married 24928
60.5%
single 11568
28.1%
divorced 4612
 
11.2%
unknown 80
 
0.2%

Most occurring characters

ValueCountFrequency (%)
r 54468
19.4%
i 41108
14.6%
e 41108
14.6%
d 34152
12.1%
m 24928
8.9%
a 24928
8.9%
n 11808
 
4.2%
s 11568
 
4.1%
g 11568
 
4.1%
l 11568
 
4.1%
Other values (6) 14156
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 281360
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 54468
19.4%
i 41108
14.6%
e 41108
14.6%
d 34152
12.1%
m 24928
8.9%
a 24928
8.9%
n 11808
 
4.2%
s 11568
 
4.1%
g 11568
 
4.1%
l 11568
 
4.1%
Other values (6) 14156
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 281360
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 54468
19.4%
i 41108
14.6%
e 41108
14.6%
d 34152
12.1%
m 24928
8.9%
a 24928
8.9%
n 11808
 
4.2%
s 11568
 
4.1%
g 11568
 
4.1%
l 11568
 
4.1%
Other values (6) 14156
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 281360
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 54468
19.4%
i 41108
14.6%
e 41108
14.6%
d 34152
12.1%
m 24928
8.9%
a 24928
8.9%
n 11808
 
4.2%
s 11568
 
4.1%
g 11568
 
4.1%
l 11568
 
4.1%
Other values (6) 14156
 
5.0%

education
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
university.degree
12168 
high.school
9515 
basic.9y
6045 
professional.course
5243 
basic.4y
4176 
Other values (3)
4041 

Length

Max length19
Median length17
Mean length12.71096
Min length7

Characters and Unicode

Total characters523539
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbasic.4y
2nd rowhigh.school
3rd rowhigh.school
4th rowbasic.6y
5th rowhigh.school

Common Values

ValueCountFrequency (%)
university.degree 12168
29.5%
high.school 9515
23.1%
basic.9y 6045
14.7%
professional.course 5243
12.7%
basic.4y 4176
 
10.1%
basic.6y 2292
 
5.6%
unknown 1731
 
4.2%
illiterate 18
 
< 0.1%

Length

2024-08-21T13:25:46.210578image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-21T13:25:46.448941image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
university.degree 12168
29.5%
high.school 9515
23.1%
basic.9y 6045
14.7%
professional.course 5243
12.7%
basic.4y 4176
 
10.1%
basic.6y 2292
 
5.6%
unknown 1731
 
4.2%
illiterate 18
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 59194
 
11.3%
i 51643
 
9.9%
s 49925
 
9.5%
. 39439
 
7.5%
o 36490
 
7.0%
r 34840
 
6.7%
h 28545
 
5.5%
c 27271
 
5.2%
y 24681
 
4.7%
n 22604
 
4.3%
Other values (15) 148907
28.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 523539
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 59194
 
11.3%
i 51643
 
9.9%
s 49925
 
9.5%
. 39439
 
7.5%
o 36490
 
7.0%
r 34840
 
6.7%
h 28545
 
5.5%
c 27271
 
5.2%
y 24681
 
4.7%
n 22604
 
4.3%
Other values (15) 148907
28.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 523539
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 59194
 
11.3%
i 51643
 
9.9%
s 49925
 
9.5%
. 39439
 
7.5%
o 36490
 
7.0%
r 34840
 
6.7%
h 28545
 
5.5%
c 27271
 
5.2%
y 24681
 
4.7%
n 22604
 
4.3%
Other values (15) 148907
28.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 523539
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 59194
 
11.3%
i 51643
 
9.9%
s 49925
 
9.5%
. 39439
 
7.5%
o 36490
 
7.0%
r 34840
 
6.7%
h 28545
 
5.5%
c 27271
 
5.2%
y 24681
 
4.7%
n 22604
 
4.3%
Other values (15) 148907
28.4%

default
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
no
32588 
unknown
8597 
yes
 
3

Length

Max length7
Median length2
Mean length3.043702
Min length2

Characters and Unicode

Total characters125364
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowunknown
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 32588
79.1%
unknown 8597
 
20.9%
yes 3
 
< 0.1%

Length

2024-08-21T13:25:46.720238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-21T13:25:46.901729image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
no 32588
79.1%
unknown 8597
 
20.9%
yes 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 58379
46.6%
o 41185
32.9%
u 8597
 
6.9%
k 8597
 
6.9%
w 8597
 
6.9%
y 3
 
< 0.1%
e 3
 
< 0.1%
s 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125364
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 58379
46.6%
o 41185
32.9%
u 8597
 
6.9%
k 8597
 
6.9%
w 8597
 
6.9%
y 3
 
< 0.1%
e 3
 
< 0.1%
s 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125364
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 58379
46.6%
o 41185
32.9%
u 8597
 
6.9%
k 8597
 
6.9%
w 8597
 
6.9%
y 3
 
< 0.1%
e 3
 
< 0.1%
s 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125364
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 58379
46.6%
o 41185
32.9%
u 8597
 
6.9%
k 8597
 
6.9%
w 8597
 
6.9%
y 3
 
< 0.1%
e 3
 
< 0.1%
s 3
 
< 0.1%

housing
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
yes
21576 
no
18622 
unknown
 
990

Length

Max length7
Median length3
Mean length2.6440225
Min length2

Characters and Unicode

Total characters108902
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowyes
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
yes 21576
52.4%
no 18622
45.2%
unknown 990
 
2.4%

Length

2024-08-21T13:25:47.106183image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-21T13:25:47.293681image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
yes 21576
52.4%
no 18622
45.2%
unknown 990
 
2.4%

Most occurring characters

ValueCountFrequency (%)
n 21592
19.8%
y 21576
19.8%
e 21576
19.8%
s 21576
19.8%
o 19612
18.0%
u 990
 
0.9%
k 990
 
0.9%
w 990
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 108902
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 21592
19.8%
y 21576
19.8%
e 21576
19.8%
s 21576
19.8%
o 19612
18.0%
u 990
 
0.9%
k 990
 
0.9%
w 990
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 108902
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 21592
19.8%
y 21576
19.8%
e 21576
19.8%
s 21576
19.8%
o 19612
18.0%
u 990
 
0.9%
k 990
 
0.9%
w 990
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 108902
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 21592
19.8%
y 21576
19.8%
e 21576
19.8%
s 21576
19.8%
o 19612
18.0%
u 990
 
0.9%
k 990
 
0.9%
w 990
 
0.9%

loan
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
no
33950 
yes
6248 
unknown
 
990

Length

Max length7
Median length2
Mean length2.2718753
Min length2

Characters and Unicode

Total characters93574
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowyes

Common Values

ValueCountFrequency (%)
no 33950
82.4%
yes 6248
 
15.2%
unknown 990
 
2.4%

Length

2024-08-21T13:25:47.500131image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-21T13:25:47.677655image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
no 33950
82.4%
yes 6248
 
15.2%
unknown 990
 
2.4%

Most occurring characters

ValueCountFrequency (%)
n 36920
39.5%
o 34940
37.3%
y 6248
 
6.7%
e 6248
 
6.7%
s 6248
 
6.7%
u 990
 
1.1%
k 990
 
1.1%
w 990
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 93574
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 36920
39.5%
o 34940
37.3%
y 6248
 
6.7%
e 6248
 
6.7%
s 6248
 
6.7%
u 990
 
1.1%
k 990
 
1.1%
w 990
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 93574
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 36920
39.5%
o 34940
37.3%
y 6248
 
6.7%
e 6248
 
6.7%
s 6248
 
6.7%
u 990
 
1.1%
k 990
 
1.1%
w 990
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 93574
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 36920
39.5%
o 34940
37.3%
y 6248
 
6.7%
e 6248
 
6.7%
s 6248
 
6.7%
u 990
 
1.1%
k 990
 
1.1%
w 990
 
1.1%

contact
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
cellular
26144 
telephone
15044 

Length

Max length9
Median length8
Mean length8.365252
Min length8

Characters and Unicode

Total characters344548
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtelephone
2nd rowtelephone
3rd rowtelephone
4th rowtelephone
5th rowtelephone

Common Values

ValueCountFrequency (%)
cellular 26144
63.5%
telephone 15044
36.5%

Length

2024-08-21T13:25:47.878119image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-21T13:25:48.060656image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
cellular 26144
63.5%
telephone 15044
36.5%

Most occurring characters

ValueCountFrequency (%)
l 93476
27.1%
e 71276
20.7%
c 26144
 
7.6%
u 26144
 
7.6%
a 26144
 
7.6%
r 26144
 
7.6%
t 15044
 
4.4%
p 15044
 
4.4%
h 15044
 
4.4%
o 15044
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 344548
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 93476
27.1%
e 71276
20.7%
c 26144
 
7.6%
u 26144
 
7.6%
a 26144
 
7.6%
r 26144
 
7.6%
t 15044
 
4.4%
p 15044
 
4.4%
h 15044
 
4.4%
o 15044
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 344548
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 93476
27.1%
e 71276
20.7%
c 26144
 
7.6%
u 26144
 
7.6%
a 26144
 
7.6%
r 26144
 
7.6%
t 15044
 
4.4%
p 15044
 
4.4%
h 15044
 
4.4%
o 15044
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 344548
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 93476
27.1%
e 71276
20.7%
c 26144
 
7.6%
u 26144
 
7.6%
a 26144
 
7.6%
r 26144
 
7.6%
t 15044
 
4.4%
p 15044
 
4.4%
h 15044
 
4.4%
o 15044
 
4.4%

month
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
may
13769 
jul
7174 
aug
6178 
jun
5318 
nov
4101 
Other values (5)
4648 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters123564
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmay
2nd rowmay
3rd rowmay
4th rowmay
5th rowmay

Common Values

ValueCountFrequency (%)
may 13769
33.4%
jul 7174
17.4%
aug 6178
15.0%
jun 5318
 
12.9%
nov 4101
 
10.0%
apr 2632
 
6.4%
oct 718
 
1.7%
sep 570
 
1.4%
mar 546
 
1.3%
dec 182
 
0.4%

Length

2024-08-21T13:25:48.259101image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-21T13:25:48.471710image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
may 13769
33.4%
jul 7174
17.4%
aug 6178
15.0%
jun 5318
 
12.9%
nov 4101
 
10.0%
apr 2632
 
6.4%
oct 718
 
1.7%
sep 570
 
1.4%
mar 546
 
1.3%
dec 182
 
0.4%

Most occurring characters

ValueCountFrequency (%)
a 23125
18.7%
u 18670
15.1%
m 14315
11.6%
y 13769
11.1%
j 12492
10.1%
n 9419
7.6%
l 7174
 
5.8%
g 6178
 
5.0%
o 4819
 
3.9%
v 4101
 
3.3%
Other values (7) 9502
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 123564
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 23125
18.7%
u 18670
15.1%
m 14315
11.6%
y 13769
11.1%
j 12492
10.1%
n 9419
7.6%
l 7174
 
5.8%
g 6178
 
5.0%
o 4819
 
3.9%
v 4101
 
3.3%
Other values (7) 9502
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 123564
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 23125
18.7%
u 18670
15.1%
m 14315
11.6%
y 13769
11.1%
j 12492
10.1%
n 9419
7.6%
l 7174
 
5.8%
g 6178
 
5.0%
o 4819
 
3.9%
v 4101
 
3.3%
Other values (7) 9502
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 123564
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 23125
18.7%
u 18670
15.1%
m 14315
11.6%
y 13769
11.1%
j 12492
10.1%
n 9419
7.6%
l 7174
 
5.8%
g 6178
 
5.0%
o 4819
 
3.9%
v 4101
 
3.3%
Other values (7) 9502
7.7%

day_of_week
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
thu
8623 
mon
8514 
wed
8134 
tue
8090 
fri
7827 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters123564
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmon
2nd rowmon
3rd rowmon
4th rowmon
5th rowmon

Common Values

ValueCountFrequency (%)
thu 8623
20.9%
mon 8514
20.7%
wed 8134
19.7%
tue 8090
19.6%
fri 7827
19.0%

Length

2024-08-21T13:25:48.738450image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-21T13:25:48.924973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
thu 8623
20.9%
mon 8514
20.7%
wed 8134
19.7%
tue 8090
19.6%
fri 7827
19.0%

Most occurring characters

ValueCountFrequency (%)
t 16713
13.5%
u 16713
13.5%
e 16224
13.1%
h 8623
7.0%
m 8514
6.9%
o 8514
6.9%
n 8514
6.9%
w 8134
6.6%
d 8134
6.6%
f 7827
6.3%
Other values (2) 15654
12.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 123564
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 16713
13.5%
u 16713
13.5%
e 16224
13.1%
h 8623
7.0%
m 8514
6.9%
o 8514
6.9%
n 8514
6.9%
w 8134
6.6%
d 8134
6.6%
f 7827
6.3%
Other values (2) 15654
12.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 123564
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 16713
13.5%
u 16713
13.5%
e 16224
13.1%
h 8623
7.0%
m 8514
6.9%
o 8514
6.9%
n 8514
6.9%
w 8134
6.6%
d 8134
6.6%
f 7827
6.3%
Other values (2) 15654
12.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 123564
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 16713
13.5%
u 16713
13.5%
e 16224
13.1%
h 8623
7.0%
m 8514
6.9%
o 8514
6.9%
n 8514
6.9%
w 8134
6.6%
d 8134
6.6%
f 7827
6.3%
Other values (2) 15654
12.7%

duration
Real number (ℝ)

Distinct1544
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean258.28501
Minimum0
Maximum4918
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size321.9 KiB
2024-08-21T13:25:49.188130image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile36
Q1102
median180
Q3319
95-th percentile752.65
Maximum4918
Range4918
Interquartile range (IQR)217

Descriptive statistics

Standard deviation259.27925
Coefficient of variation (CV)1.0038494
Kurtosis20.247938
Mean258.28501
Median Absolute Deviation (MAD)94
Skewness3.2631413
Sum10638243
Variance67225.729
MonotonicityNot monotonic
2024-08-21T13:25:49.434435image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 170
 
0.4%
85 170
 
0.4%
136 168
 
0.4%
73 167
 
0.4%
124 164
 
0.4%
87 162
 
0.4%
72 161
 
0.4%
104 161
 
0.4%
111 160
 
0.4%
106 159
 
0.4%
Other values (1534) 39546
96.0%
ValueCountFrequency (%)
0 4
 
< 0.1%
1 3
 
< 0.1%
2 1
 
< 0.1%
3 3
 
< 0.1%
4 12
 
< 0.1%
5 30
 
0.1%
6 37
0.1%
7 54
0.1%
8 69
0.2%
9 77
0.2%
ValueCountFrequency (%)
4918 1
< 0.1%
4199 1
< 0.1%
3785 1
< 0.1%
3643 1
< 0.1%
3631 1
< 0.1%
3509 1
< 0.1%
3422 1
< 0.1%
3366 1
< 0.1%
3322 1
< 0.1%
3284 1
< 0.1%

campaign
Real number (ℝ)

Distinct42
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5675925
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.9 KiB
2024-08-21T13:25:49.672797image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum56
Range55
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.7700135
Coefficient of variation (CV)1.0788369
Kurtosis36.979795
Mean2.5675925
Median Absolute Deviation (MAD)1
Skewness4.7625067
Sum105754
Variance7.672975
MonotonicityNot monotonic
2024-08-21T13:25:49.893884image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1 17642
42.8%
2 10570
25.7%
3 5341
 
13.0%
4 2651
 
6.4%
5 1599
 
3.9%
6 979
 
2.4%
7 629
 
1.5%
8 400
 
1.0%
9 283
 
0.7%
10 225
 
0.5%
Other values (32) 869
 
2.1%
ValueCountFrequency (%)
1 17642
42.8%
2 10570
25.7%
3 5341
 
13.0%
4 2651
 
6.4%
5 1599
 
3.9%
6 979
 
2.4%
7 629
 
1.5%
8 400
 
1.0%
9 283
 
0.7%
10 225
 
0.5%
ValueCountFrequency (%)
56 1
 
< 0.1%
43 2
 
< 0.1%
42 2
 
< 0.1%
41 1
 
< 0.1%
40 2
 
< 0.1%
39 1
 
< 0.1%
37 1
 
< 0.1%
35 5
< 0.1%
34 3
< 0.1%
33 4
< 0.1%

pdays
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean962.47545
Minimum0
Maximum999
Zeros15
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size321.9 KiB
2024-08-21T13:25:50.111024image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile999
Q1999
median999
Q3999
95-th percentile999
Maximum999
Range999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation186.91091
Coefficient of variation (CV)0.1941981
Kurtosis22.229463
Mean962.47545
Median Absolute Deviation (MAD)0
Skewness-4.9221899
Sum39642439
Variance34935.687
MonotonicityNot monotonic
2024-08-21T13:25:50.336422image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
999 39673
96.3%
3 439
 
1.1%
6 412
 
1.0%
4 118
 
0.3%
9 64
 
0.2%
2 61
 
0.1%
7 60
 
0.1%
12 58
 
0.1%
10 52
 
0.1%
5 46
 
0.1%
Other values (17) 205
 
0.5%
ValueCountFrequency (%)
0 15
 
< 0.1%
1 26
 
0.1%
2 61
 
0.1%
3 439
1.1%
4 118
 
0.3%
5 46
 
0.1%
6 412
1.0%
7 60
 
0.1%
8 18
 
< 0.1%
9 64
 
0.2%
ValueCountFrequency (%)
999 39673
96.3%
27 1
 
< 0.1%
26 1
 
< 0.1%
25 1
 
< 0.1%
22 3
 
< 0.1%
21 2
 
< 0.1%
20 1
 
< 0.1%
19 3
 
< 0.1%
18 7
 
< 0.1%
17 8
 
< 0.1%

previous
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.172963
Minimum0
Maximum7
Zeros35563
Zeros (%)86.3%
Negative0
Negative (%)0.0%
Memory size321.9 KiB
2024-08-21T13:25:50.510977image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.49490108
Coefficient of variation (CV)2.8613119
Kurtosis20.108816
Mean0.172963
Median Absolute Deviation (MAD)0
Skewness3.8320422
Sum7124
Variance0.24492708
MonotonicityNot monotonic
2024-08-21T13:25:50.691496image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 35563
86.3%
1 4561
 
11.1%
2 754
 
1.8%
3 216
 
0.5%
4 70
 
0.2%
5 18
 
< 0.1%
6 5
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 35563
86.3%
1 4561
 
11.1%
2 754
 
1.8%
3 216
 
0.5%
4 70
 
0.2%
5 18
 
< 0.1%
6 5
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 5
 
< 0.1%
5 18
 
< 0.1%
4 70
 
0.2%
3 216
 
0.5%
2 754
 
1.8%
1 4561
 
11.1%
0 35563
86.3%

poutcome
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
nonexistent
35563 
failure
4252 
success
 
1373

Length

Max length11
Median length11
Mean length10.453724
Min length7

Characters and Unicode

Total characters430568
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownonexistent
2nd rownonexistent
3rd rownonexistent
4th rownonexistent
5th rownonexistent

Common Values

ValueCountFrequency (%)
nonexistent 35563
86.3%
failure 4252
 
10.3%
success 1373
 
3.3%

Length

2024-08-21T13:25:50.941803image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-21T13:25:51.145260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
nonexistent 35563
86.3%
failure 4252
 
10.3%
success 1373
 
3.3%

Most occurring characters

ValueCountFrequency (%)
n 106689
24.8%
e 76751
17.8%
t 71126
16.5%
i 39815
 
9.2%
s 39682
 
9.2%
o 35563
 
8.3%
x 35563
 
8.3%
u 5625
 
1.3%
f 4252
 
1.0%
a 4252
 
1.0%
Other values (3) 11250
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 430568
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 106689
24.8%
e 76751
17.8%
t 71126
16.5%
i 39815
 
9.2%
s 39682
 
9.2%
o 35563
 
8.3%
x 35563
 
8.3%
u 5625
 
1.3%
f 4252
 
1.0%
a 4252
 
1.0%
Other values (3) 11250
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 430568
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 106689
24.8%
e 76751
17.8%
t 71126
16.5%
i 39815
 
9.2%
s 39682
 
9.2%
o 35563
 
8.3%
x 35563
 
8.3%
u 5625
 
1.3%
f 4252
 
1.0%
a 4252
 
1.0%
Other values (3) 11250
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 430568
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 106689
24.8%
e 76751
17.8%
t 71126
16.5%
i 39815
 
9.2%
s 39682
 
9.2%
o 35563
 
8.3%
x 35563
 
8.3%
u 5625
 
1.3%
f 4252
 
1.0%
a 4252
 
1.0%
Other values (3) 11250
 
2.6%

emp.var.rate
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.081885501
Minimum-3.4
Maximum1.4
Zeros0
Zeros (%)0.0%
Negative17191
Negative (%)41.7%
Memory size321.9 KiB
2024-08-21T13:25:51.323783image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-3.4
5-th percentile-2.9
Q1-1.8
median1.1
Q31.4
95-th percentile1.4
Maximum1.4
Range4.8
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation1.5709597
Coefficient of variation (CV)19.184834
Kurtosis-1.0626315
Mean0.081885501
Median Absolute Deviation (MAD)0.3
Skewness-0.72409555
Sum3372.7
Variance2.4679145
MonotonicityNot monotonic
2024-08-21T13:25:51.507314image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1.4 16234
39.4%
-1.8 9184
22.3%
1.1 7763
18.8%
-0.1 3683
 
8.9%
-2.9 1663
 
4.0%
-3.4 1071
 
2.6%
-1.7 773
 
1.9%
-1.1 635
 
1.5%
-3 172
 
0.4%
-0.2 10
 
< 0.1%
ValueCountFrequency (%)
-3.4 1071
 
2.6%
-3 172
 
0.4%
-2.9 1663
 
4.0%
-1.8 9184
22.3%
-1.7 773
 
1.9%
-1.1 635
 
1.5%
-0.2 10
 
< 0.1%
-0.1 3683
 
8.9%
1.1 7763
18.8%
1.4 16234
39.4%
ValueCountFrequency (%)
1.4 16234
39.4%
1.1 7763
18.8%
-0.1 3683
 
8.9%
-0.2 10
 
< 0.1%
-1.1 635
 
1.5%
-1.7 773
 
1.9%
-1.8 9184
22.3%
-2.9 1663
 
4.0%
-3 172
 
0.4%
-3.4 1071
 
2.6%

cons.price.idx
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.575664
Minimum92.201
Maximum94.767
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.9 KiB
2024-08-21T13:25:51.699778image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum92.201
5-th percentile92.713
Q193.075
median93.749
Q393.994
95-th percentile94.465
Maximum94.767
Range2.566
Interquartile range (IQR)0.919

Descriptive statistics

Standard deviation0.57884005
Coefficient of variation (CV)0.0061857968
Kurtosis-0.82980858
Mean93.575664
Median Absolute Deviation (MAD)0.38
Skewness-0.23088765
Sum3854194.5
Variance0.3350558
MonotonicityNot monotonic
2024-08-21T13:25:51.898249image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
93.994 7763
18.8%
93.918 6685
16.2%
92.893 5794
14.1%
93.444 5175
12.6%
94.465 4374
10.6%
93.2 3616
8.8%
93.075 2458
 
6.0%
92.201 770
 
1.9%
92.963 715
 
1.7%
92.431 447
 
1.1%
Other values (16) 3391
8.2%
ValueCountFrequency (%)
92.201 770
 
1.9%
92.379 267
 
0.6%
92.431 447
 
1.1%
92.469 178
 
0.4%
92.649 357
 
0.9%
92.713 172
 
0.4%
92.756 10
 
< 0.1%
92.843 282
 
0.7%
92.893 5794
14.1%
92.963 715
 
1.7%
ValueCountFrequency (%)
94.767 128
 
0.3%
94.601 204
 
0.5%
94.465 4374
10.6%
94.215 311
 
0.8%
94.199 303
 
0.7%
94.055 229
 
0.6%
94.027 233
 
0.6%
93.994 7763
18.8%
93.918 6685
16.2%
93.876 212
 
0.5%

cons.conf.idx
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-40.5026
Minimum-50.8
Maximum-26.9
Zeros0
Zeros (%)0.0%
Negative41188
Negative (%)100.0%
Memory size321.9 KiB
2024-08-21T13:25:52.101704image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-50.8
5-th percentile-47.1
Q1-42.7
median-41.8
Q3-36.4
95-th percentile-33.6
Maximum-26.9
Range23.9
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation4.6281979
Coefficient of variation (CV)-0.11426915
Kurtosis-0.35855831
Mean-40.5026
Median Absolute Deviation (MAD)4.4
Skewness0.30317986
Sum-1668221.1
Variance21.420215
MonotonicityNot monotonic
2024-08-21T13:25:52.320119image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
-36.4 7763
18.8%
-42.7 6685
16.2%
-46.2 5794
14.1%
-36.1 5175
12.6%
-41.8 4374
10.6%
-42 3616
8.8%
-47.1 2458
 
6.0%
-31.4 770
 
1.9%
-40.8 715
 
1.7%
-26.9 447
 
1.1%
Other values (16) 3391
8.2%
ValueCountFrequency (%)
-50.8 128
 
0.3%
-50 282
 
0.7%
-49.5 204
 
0.5%
-47.1 2458
 
6.0%
-46.2 5794
14.1%
-45.9 10
 
< 0.1%
-42.7 6685
16.2%
-42 3616
8.8%
-41.8 4374
10.6%
-40.8 715
 
1.7%
ValueCountFrequency (%)
-26.9 447
 
1.1%
-29.8 267
 
0.6%
-30.1 357
 
0.9%
-31.4 770
 
1.9%
-33 172
 
0.4%
-33.6 178
 
0.4%
-34.6 174
 
0.4%
-34.8 264
 
0.6%
-36.1 5175
12.6%
-36.4 7763
18.8%

euribor3m
Real number (ℝ)

HIGH CORRELATION 

Distinct316
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6212908
Minimum0.634
Maximum5.045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.9 KiB
2024-08-21T13:25:52.568456image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.634
5-th percentile0.797
Q11.344
median4.857
Q34.961
95-th percentile4.966
Maximum5.045
Range4.411
Interquartile range (IQR)3.617

Descriptive statistics

Standard deviation1.7344474
Coefficient of variation (CV)0.47895833
Kurtosis-1.4068026
Mean3.6212908
Median Absolute Deviation (MAD)0.108
Skewness-0.70918796
Sum149153.73
Variance3.0083078
MonotonicityNot monotonic
2024-08-21T13:25:52.809078image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.857 2868
 
7.0%
4.962 2613
 
6.3%
4.963 2487
 
6.0%
4.961 1902
 
4.6%
4.856 1210
 
2.9%
4.964 1175
 
2.9%
1.405 1169
 
2.8%
4.965 1071
 
2.6%
4.864 1044
 
2.5%
4.96 1013
 
2.5%
Other values (306) 24636
59.8%
ValueCountFrequency (%)
0.634 8
 
< 0.1%
0.635 43
0.1%
0.636 14
 
< 0.1%
0.637 6
 
< 0.1%
0.638 7
 
< 0.1%
0.639 16
 
< 0.1%
0.64 10
 
< 0.1%
0.642 35
0.1%
0.643 23
0.1%
0.644 38
0.1%
ValueCountFrequency (%)
5.045 9
 
< 0.1%
5 7
 
< 0.1%
4.97 172
 
0.4%
4.968 992
 
2.4%
4.967 643
 
1.6%
4.966 622
 
1.5%
4.965 1071
2.6%
4.964 1175
2.9%
4.963 2487
6.0%
4.962 2613
6.3%

nr.employed
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5167.0359
Minimum4963.6
Maximum5228.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.9 KiB
2024-08-21T13:25:53.018496image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum4963.6
5-th percentile5017.5
Q15099.1
median5191
Q35228.1
95-th percentile5228.1
Maximum5228.1
Range264.5
Interquartile range (IQR)129

Descriptive statistics

Standard deviation72.251528
Coefficient of variation (CV)0.013983167
Kurtosis-0.0037603757
Mean5167.0359
Median Absolute Deviation (MAD)37.1
Skewness-1.0442624
Sum2.1281988 × 108
Variance5220.2833
MonotonicityNot monotonic
2024-08-21T13:25:53.208987image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5228.1 16234
39.4%
5099.1 8534
20.7%
5191 7763
18.8%
5195.8 3683
 
8.9%
5076.2 1663
 
4.0%
5017.5 1071
 
2.6%
4991.6 773
 
1.9%
5008.7 650
 
1.6%
4963.6 635
 
1.5%
5023.5 172
 
0.4%
ValueCountFrequency (%)
4963.6 635
 
1.5%
4991.6 773
 
1.9%
5008.7 650
 
1.6%
5017.5 1071
 
2.6%
5023.5 172
 
0.4%
5076.2 1663
 
4.0%
5099.1 8534
20.7%
5176.3 10
 
< 0.1%
5191 7763
18.8%
5195.8 3683
8.9%
ValueCountFrequency (%)
5228.1 16234
39.4%
5195.8 3683
 
8.9%
5191 7763
18.8%
5176.3 10
 
< 0.1%
5099.1 8534
20.7%
5076.2 1663
 
4.0%
5023.5 172
 
0.4%
5017.5 1071
 
2.6%
5008.7 650
 
1.6%
4991.6 773
 
1.9%

y
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size40.3 KiB
False
36548 
True
4640 
ValueCountFrequency (%)
False 36548
88.7%
True 4640
 
11.3%
2024-08-21T13:25:53.385545image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Interactions

2024-08-21T13:25:41.451518image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:23.133075image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:27.055576image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:28.908620image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:30.666923image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:32.405296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:34.324145image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:36.053554image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:37.853743image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:39.723766image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:41.638031image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:23.537976image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:27.250055image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:29.110082image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:30.858408image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:32.591776image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:34.525607image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:36.252025image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:38.043236image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:39.904674image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:41.805571image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:24.418622image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:27.415612image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:29.279628image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:31.036931image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:32.777279image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:34.692161image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:36.421571image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:38.222757image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:40.084176image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:42.287284image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:25.001066image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:27.583166image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:29.457154image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:31.211465image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:32.954808image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:34.860276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:36.595108image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:38.393302image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:40.242748image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:42.457828image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:25.444886image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:27.752711image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:29.626701image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:31.385001image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:33.128341image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:35.035807image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:36.773630image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:38.567835image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:40.407308image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:42.643333image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:25.734109image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:27.943200image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:29.810232image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:31.571525image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:33.322822image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:35.225301image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:36.972100image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:38.757328image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:40.586829image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:42.807892image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:25.985435image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:28.109755image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:29.990728image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:31.734069image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:33.527275image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:35.375395image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:37.138655image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:38.924880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:40.760365image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:42.974448image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:26.262693image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:28.332162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:30.153295image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:31.902620image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:33.808523image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:35.554888image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:37.318178image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:39.236048image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:40.926920image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:43.137013image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:26.691546image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:28.587479image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:30.328848image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:32.060197image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:33.982062image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:35.723438image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:37.500693image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:39.394624image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:41.094473image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:43.304565image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:26.865104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:28.753042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:30.499369image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:32.237722image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:34.145622image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:35.887000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:37.676217image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:39.562176image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-21T13:25:41.269005image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-08-21T13:25:53.550075image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
agecampaigncons.conf.idxcons.price.idxcontactday_of_weekdefaultdurationeducationemp.var.rateeuribor3mhousingjobloanmaritalmonthnr.employedpdayspoutcomepreviousy
age1.0000.0060.1150.0450.0990.0250.146-0.0020.1170.0450.0540.0000.2490.0100.2620.0940.045-0.0010.109-0.0130.172
campaign0.0061.000-0.0020.0960.0640.0180.017-0.0810.0020.1560.1410.0220.0000.0210.0000.0470.1440.0560.047-0.0870.052
cons.conf.idx0.115-0.0021.0000.2460.4170.0450.138-0.0090.0640.2250.2370.0400.1090.0110.0720.6000.133-0.0770.369-0.1160.386
cons.price.idx0.0450.0960.2461.0000.6750.0490.1540.0030.0980.6650.4910.0690.1320.0170.0690.6760.4650.0570.386-0.2830.336
contact0.0990.0640.4170.6751.0000.0550.136-0.0360.1230.2310.1410.0850.1280.0240.0720.6090.1090.1180.242-0.2420.145
day_of_week0.0250.0180.0450.0490.0551.0000.0110.0410.0200.0300.0300.0150.0160.0060.0110.0670.027-0.0100.015-0.0090.023
default0.1460.0170.1380.1540.1360.0111.000-0.0170.1700.1770.1700.0110.1520.0020.0950.1120.1570.0800.077-0.1050.099
duration-0.002-0.081-0.0090.003-0.0360.041-0.0171.0000.000-0.069-0.0780.0000.0060.0000.0000.020-0.095-0.0830.0170.0420.377
education0.1170.0020.0640.0980.1230.0200.1700.0001.000-0.018-0.0020.0130.3600.0000.1160.095-0.010-0.0500.0420.0320.067
emp.var.rate0.0450.1560.2250.6650.2310.0300.177-0.069-0.0181.0000.9400.0520.1350.0120.0680.6590.9450.2280.379-0.4350.342
euribor3m0.0540.1410.2370.4910.1410.0300.170-0.078-0.0020.9401.0000.0520.1280.0120.0680.5520.9290.2780.417-0.4550.399
housing0.0000.0220.0400.0690.0850.0150.0110.0000.0130.0520.0521.0000.0110.7080.0090.054-0.035-0.0110.0170.0250.009
job0.2490.0000.1090.1320.1280.0160.1520.0060.3600.1350.1280.0111.0000.0100.1840.110-0.001-0.0160.1000.0070.152
loan0.0100.0210.0110.0170.0240.0060.0020.0000.0000.0120.0120.7080.0101.0000.0000.0200.0050.0010.0000.0000.000
marital0.2620.0000.0720.0690.0720.0110.0950.0000.1160.0680.0680.0090.1840.0001.0000.050-0.072-0.0390.0370.0380.054
month0.0940.0470.6000.6760.6090.0670.1120.0200.0950.6590.5520.0540.1100.0200.0501.000-0.364-0.0480.2420.1290.274
nr.employed0.0450.1440.1330.4650.1090.0270.157-0.095-0.0100.9450.929-0.035-0.0010.005-0.072-0.3641.0000.2910.412-0.4390.410
pdays-0.0010.056-0.0770.0570.118-0.0100.080-0.083-0.0500.2280.278-0.011-0.0160.001-0.039-0.0480.2911.0000.952-0.5100.325
poutcome0.1090.0470.3690.3860.2420.0150.0770.0170.0420.3790.4170.0170.1000.0000.0370.2420.4120.9521.000-0.4970.320
previous-0.013-0.087-0.116-0.283-0.242-0.009-0.1050.0420.032-0.435-0.4550.0250.0070.0000.0380.129-0.439-0.510-0.4971.0000.236
y0.1720.0520.3860.3360.1450.0230.0990.3770.0670.3420.3990.0090.1520.0000.0540.2740.4100.3250.3200.2361.000

Missing values

2024-08-21T13:25:43.625727image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-21T13:25:44.211560image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

agejobmaritaleducationdefaulthousingloancontactmonthday_of_weekdurationcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedy
056housemaidmarriedbasic.4ynononotelephonemaymon26119990nonexistent1.193.994-36.44.8575191.0no
157servicesmarriedhigh.schoolunknownnonotelephonemaymon14919990nonexistent1.193.994-36.44.8575191.0no
237servicesmarriedhigh.schoolnoyesnotelephonemaymon22619990nonexistent1.193.994-36.44.8575191.0no
340admin.marriedbasic.6ynononotelephonemaymon15119990nonexistent1.193.994-36.44.8575191.0no
456servicesmarriedhigh.schoolnonoyestelephonemaymon30719990nonexistent1.193.994-36.44.8575191.0no
545servicesmarriedbasic.9yunknownnonotelephonemaymon19819990nonexistent1.193.994-36.44.8575191.0no
659admin.marriedprofessional.coursenononotelephonemaymon13919990nonexistent1.193.994-36.44.8575191.0no
741blue-collarmarriedunknownunknownnonotelephonemaymon21719990nonexistent1.193.994-36.44.8575191.0no
824techniciansingleprofessional.coursenoyesnotelephonemaymon38019990nonexistent1.193.994-36.44.8575191.0no
925servicessinglehigh.schoolnoyesnotelephonemaymon5019990nonexistent1.193.994-36.44.8575191.0no
agejobmaritaleducationdefaulthousingloancontactmonthday_of_weekdurationcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedy
4117862retiredmarrieduniversity.degreenononocellularnovthu483263success-1.194.767-50.81.0314963.6yes
4117964retireddivorcedprofessional.coursenoyesnocellularnovfri15139990nonexistent-1.194.767-50.81.0284963.6no
4118036admin.marrieduniversity.degreenononocellularnovfri25429990nonexistent-1.194.767-50.81.0284963.6no
4118137admin.marrieduniversity.degreenoyesnocellularnovfri28119990nonexistent-1.194.767-50.81.0284963.6yes
4118229unemployedsinglebasic.4ynoyesnocellularnovfri112191success-1.194.767-50.81.0284963.6no
4118373retiredmarriedprofessional.coursenoyesnocellularnovfri33419990nonexistent-1.194.767-50.81.0284963.6yes
4118446blue-collarmarriedprofessional.coursenononocellularnovfri38319990nonexistent-1.194.767-50.81.0284963.6no
4118556retiredmarrieduniversity.degreenoyesnocellularnovfri18929990nonexistent-1.194.767-50.81.0284963.6no
4118644technicianmarriedprofessional.coursenononocellularnovfri44219990nonexistent-1.194.767-50.81.0284963.6yes
4118774retiredmarriedprofessional.coursenoyesnocellularnovfri23939991failure-1.194.767-50.81.0284963.6no

Duplicate rows

Most frequently occurring

agejobmaritaleducationdefaulthousingloancontactmonthday_of_weekdurationcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedy# duplicates
024servicessinglehigh.schoolnoyesnocellularaprtue11419990nonexistent-1.893.075-47.11.4235099.1no2
127techniciansingleprofessional.coursenononocellularjulmon33129990nonexistent1.493.918-42.74.9625228.1no2
232techniciansingleprofessional.coursenoyesnocellularjulthu12819990nonexistent1.493.918-42.74.9685228.1no2
335admin.marrieduniversity.degreenoyesnocellularmayfri34849990nonexistent-1.892.893-46.21.3135099.1no2
436retiredmarriedunknownnononotelephonejulthu8819990nonexistent1.493.918-42.74.9665228.1no2
539admin.marrieduniversity.degreenononocellularnovtue12329990nonexistent-0.193.200-42.04.1535195.8no2
639blue-collarmarriedbasic.6ynononotelephonemaythu12419990nonexistent1.193.994-36.44.8555191.0no2
741technicianmarriedprofessional.coursenoyesnocellularaugtue12719990nonexistent1.493.444-36.14.9665228.1no2
845admin.marrieduniversity.degreenononocellularjulthu25219990nonexistent-2.992.469-33.61.0725076.2yes2
947techniciandivorcedhigh.schoolnoyesnocellularjulthu4339990nonexistent1.493.918-42.74.9625228.1no2